Discovery
Discovery surfaces structure hiding in your particle data — patterns that emerge from feature similarity rather than from labels you’ve already applied.
Atlases and constellations
A Discovery job reduces particle features to a navigable map called an
. Within it, a clustering algorithm groups similar particles into s. The astronomy metaphor is deliberate: a constellation *might* reflect real biological structure, or it might be a coincidence of the feature space. You decide which.The constellation lifecycle
As you review a constellation it moves through states:
- Unreviewed — fresh algorithm output.
- Named — you’ve inspected it and given it a descriptive name.
- Mapped — it corresponds to an existing class; you link it to a collection.
- Promoted — it represents a new class; you create a collection seeded with its particles.
- Dismissed — not biologically meaningful (an artifact or noise).
Mapping links discovery to knowledge you already have; promotion turns a discovery into new knowledge.
Discovery vs Explorer
Discovery vs Explorer. Explorer visualizes what you already have, instantly and client-side. Discovery runs heavier, asynchronous compute jobs to find groupings you hadn’t defined. Explorer answers “what’s in my data?”; Discovery answers “what patterns are hiding in it?”
Quick start
- Choose a scope to embed (a series, collection, or compendium).
- Generate an and let the job run.
- Review each — name, map, promote, or dismiss it.